Research & Product Lead

San Francisco, CA · On-site · Full-time
Compensation: $150,000–$250,000 + competitive equity

About the Company

An AI research lab focused on video data — building the high-quality training datasets that frontier video models depend on. The team combines large-scale video infrastructure, novel video-understanding techniques, and diverse data sources to deliver datasets trusted by frontier AI labs, Fortune 100s, and fast-growing generative-AI startups. Already generating multiple millions in quarterly revenue with a small, fast-scaling team.
Founded 2022 · ~15 people · Industry: AI Tools / Video Data

The Role

A high-leverage generalist operator role at the intersection of product, applied research, customer engagement, and operations. You'll manage the company's most important customer accounts, drive cross-functional execution, and contribute wherever the company needs leverage — equally comfortable in a technical deep-dive, shaping roadmap priorities, navigating a commercial conversation, or building scalable internal systems.

The team is open to two profiles:

Senior track: experienced operators with existing AI-lab relationships or a background at a human-data company.
Generalist track: 1–2 years out of consulting (MBB or top tier), banking, or as an APM at an AI company, paired with an engineering degree and demonstrable technical curiosity.

Across both tracks, the universal filter is high curiosity and high EQ.

What you'll be doing

Build and manage relationships with frontier AI labs and highly technical customers
Translate customer needs into internal product and research priorities
Operate as a strategic partner to both external customers and internal technical teams
Drive cross-functional execution across research, product, operations, and partnerships
Identify operational bottlenecks and implement scalable systems and processes
Support high-priority strategic initiatives across the company
Work closely with researchers, engineers, and leadership on ambiguous, fast-moving projects
Navigate technical conversations around datasets, model performance, infrastructure, and AI workflows
Contribute wherever needed as a high-leverage generalist operator

Tech stack: Not specified

Requirements

1-2 years experience, BS in CS or equivalent
ML/AI workflow fluency, especially data pipelines
Peer-level technical depth with frontier lab researchers
High curiosity, high EQ, strong comms
Two tracks: senior (AI labs/human data) + generalist (consulting/banking/APM + eng degree)

Green Flags

Existing trusted relationships at frontier AI labs or AI model companies, direct ecosystem trust is the highest-signal background for the senior track.
Background at a human data company (Scale, Surge, Sapien, Mercor, Invisible, Toloka, Snorkel, Defined.ai). Direct adjacency to the company's market and customer base.
For the junior track: 1–2 years out of consulting (MBB or top tier), banking, or as an APM at an AI company, paired with an engineering degree. Scrappy, high curiosity, high EQ.
Comfortable debugging a data pipeline question and navigating a commercial expansion conversation in the same hour. Treats relationship management as a craft.
Prior early-stage startup experience, especially as an early hire who saw a customer-facing function get built from zero.

Red Flags

Pure account manager or CSM background with no technical depth. Cannot hold a peer-level conversation with researchers at a frontier lab.
Pure technical operator with no client-facing or commercial instinct. The role is half relationship, half technical.
Career enterprise SaaS sales without AI/ML domain context. The buyer is too technical for product framing alone.
Cannot or will not work 5 days/week in person at the SF HQ. Non-negotiable.
For the junior track: 1-2 YoE without an engineering degree or without demonstrable technical curiosity. Must be able to ramp into ML/AI workflow conversations quickly.

Why Join

Work directly with the most technically demanding organizations in the world — frontier AI labs and Fortune 100s
Genuine generalist scope across product, applied research, customer engagement, and operations
Early-stage, high-ownership role on a small team already generating real revenue

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